Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Accurate Prediction of Quality of Transmission with Dynamically Configurable Optical Impairment Model

Not Accessible

Your library or personal account may give you access

Abstract

We propose a dynamically configurable optical impairment model for a physical layer abstraction enabling physical parameters learning in multi-vendor networks. We experimentally demonstrate quality of transmission prediction in mesh networks with 0.6 dB Q-factor accuracy.

© 2017 Optical Society of America

PDF Article
More Like This
Considering transmission impairments in configuring wavelength routed optical networks

R. Cardillo, V. Curri, and M. Mellia
OFG6 Optical Fiber Communication Conference (OFC) 2006

Holistic Optical Network Optimization across Network and Physical Layers

Martin Bouda, Shoichiro Oda, Xi Wang, Paparao Palacharla, and Tadashi Ikeuchi
PTu2D.1 Photonics in Switching (PS) 2017

Demonstration of Continuous Improvement in Open Optical Network Design by QoT Prediction using Machine Learning

Martin Bouda, Shoichiro Oda, Yuichi Akiyama, Denis Paunovic, Takeshi Hoshida, Paparao Palacharla, and Tadashi Ikeuchi
M3Z.2 Optical Fiber Communication Conference (OFC) 2019

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved